Sustainability assessment of Iranian petrochemical companies in stock exchange: A data envelopment analysis-based approach

The assessment of efficiency is always of particular importance according to different indicators from different perspectives. There are various techniques for evaluating petrochemical companies, among which the data envelopment analysis technique is one of the best techniques that can be used to calculate the relative efficiency of a set of decision-making units with network structures. In the present paper, seven petrochemical companies listed in the Iranian stock exchange were analysed. These companies were evaluated in terms of financial performance and sustainable development, and their relative efficiency was calculated during 2015–2016. According to the obtained results, only Marun Petrochemical Co. was found to be efficient in all areas and years. The results also showed that four companies were efficient in financial terms over the period under study. In the general conclusion regarding the companies' performance, Marun was ranked first, Jam was ranked second, and Zagros was ranked third.     

Minimum Initial Marking Estimation of Labeled Petri Nets Based on GRASP Inspired Method (GMIM)

Abstract

This paper deals with the problem of estimating the Minimum Initial Marking (MIM) of Labeled Petri Nets (L-PN). By the observation of a sequence of labels, we determine the set of possible MIMs related to a given L-PN through an approach based on GRASP (Greedy Randomized Adaptive Search Procedure) inspired method – GMIM. The objective is to get the maximum of feasible MIMs by exploring the search space and giving best solutions for real time cyber systems in short time. We consider four basic assumptions during the reasoning: (i) the L-PN structure is known; (ii) for each transition of L-PN, a label is associated, (iii) the label sequence is known, and (iv) all transitions of L-PN are observable. We show the validity and efficiency of our approach by applying the proposed GMIM metaheuristic to two validation examples: Initialization of two parallel machines (example widely cited in literature) and resources allocation in a monitoring problem via mobile robot network.     

Driver information system: a combination of augmented reality,deep learning and vehicular Ad-hoc networks

Improving traffic safety is one of the important goals of Intelligent Transportation Systems (ITS). In vehicle-based safety systems, it is more desirable to prevent an accident than to reduce severity of injuries. Critical traffic problems such as accidents and traffic congestion require the development of new transportation systems. Research in perceptual and human factors assessment is needed for relevant and correct display of this information for maximal road traffic safety as well as optimal driver comfort. One of the solutions to prevent accidents is to provide information on the surrounding environment of the driver. Augmented Reality Head-Up Display (AR-HUD) can facilitate a new form of dialogue between the vehicle and the driver; and enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads. In this paper, we propose a fast deep-learning-based object detection approaches for identifying and recognizing road obstacles types, as well as interpreting and predicting complex traffic situations. A single convolutional neural network predicts region of interest and class probabilities directly from full images in one evaluation. We also investigated potential costs and benefits of using dynamic conformal AR cues in improving driving safety. A new AR-HUD approach to create real-time interactive traffic animations was introduced in terms of types of obstacle, rules for placement and visibility, and projection of these on an in-vehicle display. The novelty of our approach is that both global and local context information are integrated into a unified framework to distinguish the ambiguous detection outcomes, enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads.     

Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach

Resource sharing, as a coordination mechanism, can mitigate disruptions in supply and changes in demand. It is particularly crucial for platelets because they have a short lifespan and need to be transferred and allocated within a limited time to prevent waste or shortages. Thus, a coordinated model comprised of a mixed vertical-horizontal structure, for the logistics of platelets, is proposed for disaster relief operations in the response phase. The aim of this research is to reduce the wastage and shortage of platelets due to their critical role in wound healing. We present a bi-objective location-allocation robust possibilistic programming model for designing a two-layer coordinated organization strategy for multi-type blood-derived platelets under demand uncertainty. Computational results, derived using a heuristic ε-constraint algorithm, are reported and discussed to show the applicability of the proposed model. The experimental results indicate that surpluses and shortages in platelets remarkably declined following instigation of a coordinated disaster relief operation.     

Local commute-time guided MDS for 3D non-rigid object retrieval

In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets.     

Optimal Newton–Secant like methods without memory for solving nonlinear equations with its dynamics

We construct two optimal Newton–Secant like iterative methods for solving nonlinear equations. The proposed classes have convergence order four and eight and cost only three and four function evaluations per iteration, respectively. These methods support the Kung and Traub conjecture and possess a high computational efficiency. The new methods are illustrated by numerical experiments and a comparison with some existing optimal methods. We conclude with an investigation of the basins of attraction of the solutions in the complex plane.     

Ferroelectric solid solution Li1 − xTa1 − xWxO3 as potential photocatalysts in microbial fuel cells: Effect of the W content

Microbial fuel cells (MFCs) are bio-electrochemical systems that can directly convert the chemical energy contained in an effluent into bioelectricity by the action of microorganisms. The performance of these devices is heavily impacted by the choice of the material that forms the cathode. This work focuses on the assessment of ferroelectric and photocatalytic materials as a new class of non-precious catalysts for MFC cathode construction. A series of cathodes based on mixed oxide solid solution of LiTaO₃ with WO₃ formulated as Li_1-xTa_1-xW_xO₃ (x=0, 0.10, 0.20 and 0.25), were prepared and investigated in MFCs. The catalyst phases were synthesized, identified and characterized by DRX, PSD, MET and UV-Vis absorption spectroscopy. The cathodes were tested as photoelectrocatalysts in the presence and in the absence of visible light in devices fed with industrial wastewater. The results revealed that the catalytic activity of the cathodes strongly depends on the ratio of substitution of W⁶⁺ in the LiTaO₃ matrix. The maximum power densities generated by the MFC working with this series of cathodes increased from 60.45 mW·m^-3 for x=0.00 (LiTaO₃) to 107.2 mW·m^-3 for x=0.10, showing that insertion of W⁶⁺ in the tantalate matrix can improve the photocatalytic activity of this material. Moreover, MFCs operating under optimal conditions were capable of reducing the load of chemical oxygen demand by 79% (COD_initial=1030 mg·L^-1).     

Microwave-assisted extractive catalytic-oxidative desulfurization of diesel fuel via a VO(acac)2/ionic liquid system with H2O2 and H2SO4 as oxidizing agents

The removal of sulfur compounds from transportation fuel is an important aspect for protecting environment and for fuel cell applications. On the other hand, an innovative way to remove the sulfur is necessary because clean low-sulfur diesel is more widely used in the world today. In this work, we studied the effect of microwave irradiation power and time on the extractive catalytic oxidative desulfurization (ECODS) process of diesel fuel model (40 mL with initial S-content of 450 ppm), using vanadyl acetylacetonate (VO(acac)₂) as a catalyst and N-carboxymethylpyridine hydrosulphate ionic liquid ([CH₂COOHPy][HSO₄] IL) as an extractant, and hydrogen peroxide (H₂O₂) as an oxidant agent. The optimal microwave-assisted extractive catalytic – oxidative desulfurization (MECODS) experimental conditions were as follows: microwave irradiation power?=?500?W, microwave irradiation time?=?90?s, IL/diesel volume ratio?=?1:10, VO(acaca)₂/diesel mass ratio?=?0.5?wt%, and H₂O₂ volume?=?1 mL. Under these conditions, the sulfur content in commercial diesel fuel was reduced from 450 to 60?ppm (sulfur removal efficiency of 86.67%), which was superior to that of the simple oxidation with no IL (22.6%) or oxidation with not including catalyst (11.3%), and without affecting the physicochemical properties of diesel fuel. The catalytic system VO(acac)₂/IL can be recycled 5 times with merely a negligible loss in activity. Based on these experimental results, a MECODS mechanism was proposed. Ultra-deep desulfurization with 99.1% of sulfur removal efficiency was reached, using MECODS reaction under optimum conditions by adding 3?mL of H₂SO₄ (0.1?N) to the main reaction. This highest sulfur removal efficiency can be attributed to the synergetic effect between microwave activation heating energy and the additional protonation, which multiplied the sulfones’ (BTO₂s and DBTO₂s) formation pathways and thus accelerated the desulfurization reactions.     

Performance evaluation of legacy QCN for multicast and multiple unicast traffic transmission

A multicast congestion control scheme is an interesting feature to control group communication applications such as teleconferencing tools and information dissemination services. This paper addresses a comparison between multiple unicast and multicast traffic congestion control for Carrier Ethernet. In this work, we proposed to study the quantized congestion notification (QCN), which is a layer 2 congestion control scheme, in the case of multicast traffic and multiple unicast traffic. Indeed, the QCN has recently been standardized as the IEEE 802.1Qau Ethernet Congestion Notification standard. This scheme is evaluated through simulation experiments, which are implemented by the OMNeT++ framework. This paper evaluates the reaction point start time congestion detection, feedback rate, loss rate, stability, fairness and scalability performance of the QCN for multicast traffic transmission and multiple unicast traffic transmission. This paper also draws a parallel between QCN for multicast traffic transmission and that for multiple unicast traffic transmission. Despite the benefit of integrating the multicast traffic, results show that performance could degrade when the network scales up. The evaluation results also show that it is probable that the feedback implosion problem caused by the bottlenecks could be solved if we choose to set the queue parameter Qeq threshold value at a high value, 75% of the queue capacity for instance. Copyright © 2016 John Wiley & Sons, Ltd.     

Interpreting the hydrogen adsorption on organic groups functionalized MOF-5s by statistical physics model

The hydrogen adsorption isotherms at equilibrium on four adsorbents (MOF-5 and three modified MOF-5s named, CH₃-MOF-5, Br-MOF-5 and Cl-MOF-5) were studied using a monolayer model with four adsorption sites energies. The analytical expression of this model was developed using the grand canonical ensemble in statistical physics by taking some working hypotheses involving some physicochemical parameters which can describe the adsorption process. These parameters are: four numbers of hydrogen adsorbed molecules per site (n₁, n₂, n₃ and n₄), four receptor site densities (N_M1, N_M2, N_M3 and N_M4), four saturation adsorbed quantities (Q₁, Q₂, Q₃ and Q₄) and four adsorption energies (??₁, ??₂, ??₃ and ??₄). The evolutions of these parameters in relation with temperature were discussed to understand and interpret the adsorption process at different temperatures. Fitting results revealed that the adsorption of hydrogen on MOF-5 is an exothermic physisorption process. The adsorption surface is inhomogeneous with many site energies. The fitting of the adsorption site is achieved by an aggregate of hydrogen molecules. The adopted model expression is used to derive the thermodynamic potential functions which govern the sorption mechanism such as entropy S_a, free enthalpy of Gibbs G and internal energy E_int.